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Cache-Oblivious parallel SIMD Viterbi decoding for sequence search in HMMER.

Miguel Ferreira, Nuno Roma, Luis M S Russo1

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Summary
This summary is machine-generated.

A new Cache-Oblivious Parallel SIMD Viterbi (COPS) implementation offers a faster alternative for biological sequence analysis using Hidden Markov Models (HMMs). This optimized Viterbi decoding achieves up to two times speedup, outperforming existing HMMER3 methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Optimization

Background:

  • Hidden Markov Models (HMMs) are crucial for biological sequence analysis.
  • HMMER software utilizes the Viterbi decoding algorithm for homology detection.
  • Existing Viterbi implementations leverage Intel SSE2 for parallelization.

Purpose of the Study:

  • To develop a novel, optimized SIMD vectorization of the Viterbi decoding algorithm.
  • To enhance computational efficiency in biological sequence analysis.
  • To improve cache locality and processing throughput.

Main Methods:

  • Implemented a new SSE2 inter-task parallelization strategy for Viterbi decoding.
  • Introduced a novel Markov model partitioning for improved cache utilization.
  • Optimized emission score loading for consistent processing.

Main Results:

  • The new implementation achieves constant processing throughput irrespective of cache size or model dimension.
  • Evaluated against HMMER3, the proposed method demonstrates competitive performance.
  • Achieved processing speedups up to two times faster than HMMER3's ViterbiFilter.

Conclusions:

  • The Cache-Oblivious Parallel SIMD Viterbi (COPS) is a highly competitive alternative to HMMER3.
  • COPS provides consistent performance and significant speedups for DNA and protein datasets.
  • This optimized Viterbi algorithm enhances the efficiency of HMM-based bioinformatics tools.